We are supporting our partners SSEN Transmission as part of the 2026 Energy Innovation Basecamp.
Innovative people and businesses are invited by the Energy Networks Association (ENA) to propose ideas and solutions to solve problems suggested by Britain’s electricity and gas networks.
If you would like our support in submitting your application, please contact us.
Please note the deadline for this opportunity is 13 March 2026.
What’s the Problem?
Artificial intelligence and related technologies (eg machine learning) are advancing rapidly and can now solve very complex problems in other industries (for example, Protein Folding in Biochemistry). It seems likely that AI can solve a variety of issues in the energy industry. We have already made progress, and solutions that have been explored are summarised in Table 1 at the end of this document:
While some of these use cases are well developed, many projects and initiatives focus on specific niches. It is not possible to say that the regulated networks are engaged in a coordinated program of research and development leading to holistic, shared solutions.
As we move towards net-zero, the energy network is becoming much more complicated and distributed, combining highly dispersed renewable energy, nuclear and small nuclear reactors, and more traditional base-load generation. This requires a proliferation of supporting services, including energy storage and energy stability. This needs to be supported by a greatly expanded electricity transmission and distribution network. In addition, the gas network may need to be converted to support the transmission of Hydrogen across the UK, whether on a localised or national level. This is further complicated by a much more dynamic energy market, where customers are incentivised to manage consumption to balance the load better and reduce constraint costs. The question is whether we are designing and building the optimal energy system with assets in the right locations to achieve affordability and sustainability. Is this type of optimisation problem only solvable through Artificial (General) Intelligence, machine learning and high-powered computing.
What is Required?
We are looking for proposals for a coordinated research program, or possibly a dedicated research centre or centre of excellence, to answer the question: Can we use Artificial General Intelligence to better design, build, and manage the integrated energy system of the future?
The AGI should be designed to analyse the problem and generate a set of network design options that minimise cost, minimise use of environmental capital, maximise energy efficiency, and maximise social utility.
How could this research program be coordinated across the entire UK energy network? What are the use cases and problems that should be solved in the journey to solve the problem of designing the most efficient energy network possible? We are interested in both national and regionalised solutions for the UK energy market.
This challenge is very ambitious, but very deliberately so. This is much more about the big picture optimisation of the UK network. The ambition is a well-funded R&D institute (like the HVDC Centre) but focused on AI applied to energy systems, funded and supported by the whole energy industry in the UK.
What are the Constraints?
We are not looking for a single niche use case, but more for proposals that will explore the best way to set up a coordinated program of research and development, which will allow agile and rapid growth of new tools. The overall goal should be ambitious, to employ AGI to design the most efficient possible UK network that offers best value for the customer. While ambitious, we should examine how uses cases can be developed that bring more immediate benefits to the consumer while also working towards much mor ambitious tools. ambitious, we should examine how use cases can be developed that bring more immediate benefits to the consumer while also working towards much more.
In the first phase of the project we would like to answer the following questions:
- Do we need a dedicated (well funded) and shared centre of excellence for the application of AI (and other data analytic tools) in Energy Networks? What is the evidence for the recommendation
- If the answer to (1) is yes, what are the options and models for setting up a centre of excellence? How should any centre of excellence be governed
- Who should be involved in any proposed centre of excellence?
- What can we learn from other industries, and what would be best practice for agile use case development that can be shared and adopted by all energy networks in the UK?
- How can the UK benefit (in terms of general economic growth) from a dedicated centre of excellence?
- What level of funding is required to make a centre of excellence work?
Who are the Key Players?
The key players are all the regulated energy networks, energy developers, and experts in the application of AGI and Machine Learning, and in the use of high-powered computing. We are interested in collaborative proposals that involve partnerships among networks, academia, research institutes, and experts in AGI and high-performance computing. We would welcome proposals from private and public research institutes and consider part-funding a dedicated research program.
There is also a research consortium, mainly USA based under EPRI. Proposals should consider how to best existing programs of research from around the world.
Does the Problem Build on Existing or Previous Projects?
This problem statement is based on a proposal developed by a People Plant Interface (PPI) Working Group as part of the Pathway to 2030 SHW Steering Group. The proposal, PPI Memorandum of Use (MoU), seeks to establish a formal agreement between contract partners to collaborate on trialling and adopting new safety technologies. This initiative builds on the existing structure of the P2030/ASTI schemes and aims to formalise a process for continuous safety improvement.